Systematic Relationships Between Lidar Observables and Sizes And Mineral Composition Of Dust Aerosols

The physical and chemical properties of soil dust aerosol particles fundamentally affect their interaction with climate, including shortwave absorption and radiative forcing, nucleation of cloud droplets and ice crystals, heterogeneous formation of sulfates and nitrates on the surface of dust particles, and atmospheric processing of iron into bioavailable forms that increase the productivity of marine phytoplankton. Lidar measurements, such as extinction-to-backscatter, color and depolarization ratios, are frequently used to distinguish between aerosol types with different physical and chemical properties. The chemical composition of aerosol particles determines their complex refractive index, hence affecting their backscattering properties. Here we present a study on how dust aerosol backscattering and depolarization properties at wavelengths of 355, 532 and 1064 nm are related to size and complex refractive index, which varies with the mineral composition of the dust. Dust aerosols are represented by collections of spheroids with a range of prolate and oblate aspect ratios and their optical properties are obtained using T-matrix calculations. We find simple, systematic relationships between lidar observables and the dust size and complex refractive index that may aid the use of space-based or airborne lidars for direct retrieval of dust properties or for the evaluation of chemical transport models using forward simulated lidar variables. In addition, we present first results on the spatial variation of forward-simulated lidar variables based on a dust model that accounts for the atmospheric cycle of eight different mineral types plus internal mixtures of seven mineral types with iron oxides, which was recently implemented in the NASA GISS Earth System ModelE2

How the Assumed Size Distribution of Dust Minerals Affects the Predicted Ice Forming Nuclei

The formation of ice in clouds depends on the availability of ice forming nuclei (IFN). Dust aerosol particles are considered the most important source of IFN at a global scale. Recent laboratory studies have demonstrated that the mineral feldspar provides the most efficient dust IFN for immersion freezing and together with kaolinite for deposition ice nucleation, and that the phyllosilicates illite and montmorillonite (a member of the smectite group) are of secondary importance.A few studies have applied global models that simulate mineral specific dust to predict the number and geographical distribution of IFN. These studies have been based on the simple assumption that the mineral composition of soil as provided in data sets from the literature translates directly into the mineral composition of the dust aerosols. However, these tables are based on measurements of wet-sieved soil where dust aggregates are destroyed to a large degree. In consequence, the size distribution of dust is shifted to smaller sizes, and phyllosilicates like illite, kaolinite, and smectite are only found in the size range 2 m. In contrast, in measurements of the mineral composition of dust aerosols, the largest mass fraction of these phyllosilicates is found in the size range 2 m as part of dust aggregates. Conversely, the mass fraction of feldspar is smaller in this size range, varying with the geographical location. This may have a significant effect on the predicted IFN number and its geographical distribution.An improved mineral specific dust aerosol module has been recently implemented in the NASA GISS Earth System ModelE2. The dust module takes into consideration the disaggregated state of wet-sieved soil, on which the tables of soil mineral fractions are based. To simulate the atmospheric cycle of the minerals, the mass size distribution of each mineral in aggregates that are emitted from undispersed parent soil is reconstructed. In the current study, we test the null-hypothesis that simulating the presence of a large mass fraction of phyllosilicates in dust aerosols in the size range 2 m, in comparison to a simple model assumption where this is neglected, does not yield a significant effect on the magnitude and geographical distribution of the predicted IFN number. Results from sensitivity experiments are presented as well

How the Emitted Size Distribution and Mixing State of Feldspar Affect Ice Nucleating Particles in a Global Model

The effect of aerosol particles on ice nucleation and, in turn, the formation of ice and mixed phase clouds is recognized as one of the largest sources of uncertainty in climate prediction. We apply an improved dust mineral specific aerosol module in the NASA GISS Earth System ModelE, which takes into account soil aggregates and their fragmentation at emission as well as the emission of large particles. We calculate ice nucleating particle concentrations from K-feldspar abundance for an active site parameterization for a range of activation temperatures and external and internal mixing assumption. We find that the globally averaged INP concentration is reduced by a factor of two to three, compared to a simple assumption on the size distribution of emitted dust minerals. The decrease can amount to a factor of five in some geographical regions. The results vary little between external and internal mixing and different activation temperatures, except for the coldest temperatures. In the sectional size distribution, the size range 24 micrometer contributes the largest INP number

Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger

Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels. Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January–May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data. Results: At the national level, a model using early incidence in December and averaged November–December zonal wind provided the best fit (pseudo-R2 = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R2 = 0.41). Conclusions: We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic

Predicting the mineral composition of dust aerosols: Insights from elemental composition measured at the Izaña Observatory

Regional variations of dust mineral composition are fundamental to climate impacts but generally neglected in climate models. A challenge for models is that atlases of soil composition are derived from measurements following wet sieving, which destroys the aggregates potentially emitted from the soil. Aggregates are crucial to simulating the observed size distribution of emitted soil particles. We use an extension of brittle fragmentation theory in a global dust model to account for these aggregates. Our method reproduces the size-resolved dust concentration along with the approximately size-invariant fractional abundance of elements like Fe and Al in the decade-long aerosol record from the Izaña Observatory, off the coast of West Africa. By distinguishing between Fe in structural and free forms, we can attribute improved model behavior to aggregation of Fe and Al-rich clay particles. We also demonstrate the importance of size-resolved measurements along with elemental composition analysis to constrain models.This research was supported by the Department of Energy (DE-SC0006713), the NASA Modeling, Analysis and Prediction Program, and the Aerosol Global Atmospheric Watch program of Izaña Observatory, which has been funded by AEMET and several research projects of the Ministry of Economy and Competitiveness of Spain and the European Regional Development Fund (ERDF) including POLLINDUST (CGL2011-26259) and AEROATLAN (CGL2015-66229-P)

Why Is Improvement of Earth System Models so Elusive? Challenges and Strategies from Dust Aerosol Modeling

Past decades have seen an accelerating increase in computing efficiency, while climate models are representing a rapidly widening set of physical processes. Yet simulations of some fundamental aspects of climate like precipitation or aerosol forcing remain highly uncertain and resistant to progress. Dust aerosol modeling of soil particles lofted by wind erosion has seen a similar conflict between increasing model sophistication and remaining uncertainty. Dust aerosols perturb the energy and water cycles by scattering radiation and acting as ice nuclei, while mediating atmospheric chemistry and marine photosynthesis (and thus the carbon cycle). These effects take place across scales from the dimensions of an ice crystal to the planetary-scale circulation that disperses dust far downwind of its parent soil. Representing this range leads to several modeling challenges. Should we limit complexity in our model, which consumes computer resources and inhibits interpretation? How do we decide if a process involving dust is worthy of inclusion within our model? Can we identify a minimal representation of a complex process that is efficient yet retains the physics relevant to climate? Answering these questions about the appropriate degree of representation is guided by model evaluation, which presents several more challenges. How do we proceed if the available observations do not directly constrain our process of interest? (This could result from competing processes that influence the observed variable and obscure the signature of our process of interest.) Examples will be presented from dust modeling, with lessons that might be more broadly applicable. The end result will either be clinical depression or there assuring promise of continued gainful employment as the community confronts these challenges

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ∼ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ∼ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission

Dangerous human-made interference with climate: a GISS modelE study

We investigate the issue of "dangerous human-made interference with climate" using simulations with GISS modelE driven by measured or estimated forcings for 1880–2003 and extended to 2100 for IPCC greenhouse gas scenarios as well as the "alternative" scenario of Hansen and Sato (2004). Identification of "dangerous" effects is partly subjective, but we find evidence that added global warming of more than 1°C above the level in 2000 has effects that may be highly disruptive. The alternative scenario, with peak added forcing ~1.5 W/m2 in 2100, keeps further global warming under 1°C if climate sensitivity is ~3°C or less for doubled CO2. The alternative scenario keeps mean regional seasonal warming within 2σ (standard deviations) of 20th century variability, but other scenarios yield regional changes of 5–10σ, i.e. mean conditions outside the range of local experience. We conclude that a CO2 level exceeding about 450 ppm is "dangerous", but reduction of non-CO2 forcings can provide modest relief on the CO2 constraint. We discuss three specific sub-global topics: Arctic climate change, tropical storm intensification, and ice sheet stability. We suggest that Arctic climate change has been driven as much by pollutants (O3, its precursor CH4, and soot) as by CO2, offering hope that dual efforts to reduce pollutants and slow CO2 growth could minimize Arctic change. Simulated recent ocean warming in the region of Atlantic hurricane formation is comparable to observations, suggesting that greenhouse gases (GHGs) may have contributed to a trend toward greater hurricane intensities. Increasing GHGs cause significant warming in our model in submarine regions of ice shelves and shallow methane hydrates, raising concern about the potential for accelerating sea level rise and future positive feedback from methane release. Growth of non-CO2 forcings has slowed in recent years, but CO2 emissions are now surging well above the alternative scenario. Prompt actions to slow CO2 emissions and decrease non-CO2 forcings are required to achieve the low forcing of the alternative scenario

Cloud Cover Increase with Increasing Aerosol Absorptivity: A Counterexample to the Conventional Semidirect Aerosol Effect

We reexamine the aerosol semidirect effect using a general circulation model and four cases of the single-scattering albedo of dust aerosols. Contrary to the expected decrease in low cloud cover due to heating by tropospheric aerosols, we find a significant increase with increasing absorptivity of soil dust particles in regions with high dust load, except during Northern Hemisphere winter. The strongest sensitivity of cloud cover to dust absorption is found over land during Northern Hemisphere summer. Here even medium and high cloud cover increase where the dust load is highest. The cloud cover change is directly linked to the change in relative humidity in the troposphere as a result of contrasting changes in specific humidity and temperature. More absorption by aerosols leads to larger diabatic heating and increased warming of the column, decreasing relative humidity. However, a corresponding increase in the specific humidity exceeds the temperature effect on relative humidity. The net effect is more low cloud cover with increasing aerosol absorption. The higher specific humidity where cloud cover strongly increases is attributed to an enhanced convergence of moisture driven by dust radiative heating. Although in some areas our model exhibits a reduction of low cloud cover due to aerosol heating consistent with the conventional description of the semidirect effect, we conclude that the link between aerosols and clouds is more varied, depending also on changes in the atmospheric circulation and the specific humidity induced by the aerosols. Other absorbing aerosols such as black carbon are expected to have a similar effect